Factors Affecting the Outlook for Medium-term to Long-term Growth in China

2016 ◽  
Vol 24 (5) ◽  
pp. 20-41 ◽  
Author(s):  
Justin Yifu Lin ◽  
Guanghua Wan ◽  
Peter J. Morgan
Keyword(s):  
Medium Term ◽  
Factors Affecting

scholarly journals Coconut Cryopreservation: Present Status and Future Prospects

CORD ◽  
2017 ◽  
Vol 33 (1) ◽  
pp. 21
Author(s):  
Iroshini Welewanni ◽  
Dharshani Bandupriya
Keyword(s):  
Plant Species ◽  
Medium Term ◽  
Future Directions ◽  
Factors Affecting ◽  
Cryopreservation Techniques ◽  
Long Term Preservation ◽  
Small Holder ◽  
Selection Of ◽  
Very Low Temperature

Coconut is one of the most important small holder crops worldwide. Conservation of coconut as seeds or field gene banks is not effective due to a range of limitations. Cryopreservation, which is the conservation of living propagules at very low temperature (-196ºC), is the only method available currently for the long-term conservation of germplasm for problem plant species such as recalcitrant and vegetatively propagated plant species. This review summarizes different cryopreservation techniques that have been published from 1984 until the present in relation to different coconut material; it includes a brief discussion about short and medium-term cryopreservation before describing long-term preservation. It discusses factors affecting the process and success of cryopreservation, such as selection of plant material, pre-culture of tissues, osmoprotection, dehydration, cryo-storage, thawing and post-culturing of tissues, and finally to plants. The review also describes histological and ultra-structural studies on and the use of molecular markers to assess genetic stability after cryopreservation. Limitations and future directions related to coconut cryopreservation are discussed. Additional experiments are identified that will need to be undertaken to improve our understanding of the different cryopreservation methods.

Aspects of the Evolution of the Romanian Tourists’ Preferences Concerning the Domestic Tourist Destinations

2019 ◽  
Vol 10 (1) ◽  
pp. 21-28
Author(s):  
Aniela Bălăcescu ◽  
Radu Șerban Zaharia
Keyword(s):  
Logistic Model ◽  
Local Level ◽  
Medium Term ◽  
Tourist Destinations ◽  
Tourism Management ◽  
Production And Consumption ◽  
On Line ◽  
The One ◽  
Long Term Studies

Abstract Tourist services represent a category of services in which the inseparability of production and consumption, the inability to be storable, the immateriality, and last but not least non-durability, induces in tourism management a number of peculiarities and difficulties. Under these circumstances the development of medium-term strategies involves long-term studies regarding on the one hand the developments and characteristics of the demand, and on the other hand the tourist potential analysis at regional and local level. Although in the past 20 years there has been tremendous growth of on-line booking made by household users, the tour operators agencies as well as those with sales activity continue to offer the specific services for a large number of tourists, that number, in the case of domestic tourism, increased by 1.6 times in case of the tour operators and by 4.44 times in case of the agencies with sales activity. At the same time, there have been changes in the preferences of tourists regarding their holiday destinations in Romania. Started on these considerations, paper based on a logistic model, examines the evolution of the probabilities and scores corresponding to the way the Romanian tourists spend their holidays on the types of tourism agencies, actions and tourist areas in Romania.

INFORMATION SUPPORT FOR MAKING MANAGEMENT DECISIONS IN THE FIELD OF PREVENTION OF EYE DISEASES AT STUDENTS BASED ON MULTI-VARIANT SIMULATION AND FORECASTING

2020 ◽  
pp. 147-153
Author(s):  
Юлия Владимировна Татаркова ◽  
Татьяна Николаевна Петрова ◽  
Олег Валериевич Судаков ◽  
Александр Юрьевич Гончаров ◽  
Ольга Николаевна Крюкова
Keyword(s):  
Preventive Measures ◽  
Influential Factors ◽  
Eye Diseases ◽  
Information Support ◽  
Factor Space ◽  
Factors Affecting ◽  
Eye Fatigue ◽  
Modeling And Forecasting ◽  
Medical Examinations

В настоящей статье представлен обзор основных решений, доступных сегодня для формирования как краткосрочных, так и долгосрочных проекций заболеваемости болезней глаза и его придаточного аппарата в студенческой среде. С другой стороны, существует ряд проблем, связанных с многообразием факторов, влияющих на заболеваемость, статистической необоснованностью и противоречивостью имеющихся результатов анализа данных. Представлены результаты математического моделирования зависимости показателя заболеваемости от наиболее влиятельных факторов образовательной и социальной среды. Перечислены важнейшие направления разработки математических моделей распространения заболеваемости. С помощью разработанного программного комплекса проведена серия вычислительных экспериментов по оценке и прогнозированию заболеваемости обучающихся в вузах разного профиля. Показана эффективность применения методики многовариантного моделирования и прогнозирования, указаны их ограничения и возможности практического применения. По расположению обобщенной области благоприятного прогноза в факторном пространстве можно определить время воздействия неблагоприятных для зрения факторов, которое должно составлять не более 10 ... 11 часов в сутки, количество профилактических мероприятий должно составлять не менее 3 ... 4. При этом риск развития миопии составит не более 0,4, вероятность усталости глаз за компьютером составит не более 0,4, вероятность дискомфорта глаз на занятиях составит не более 0,15. Исходя из характера прогноза, определяется длительность диспансерного наблюдения, а также потребность профилактических мероприятий по устранению или ослаблению действия неблагоприятно влияющих социально-гигиенических и медико-биологических факторов конкретного больного. Использование прогностической матрицы в практическом здравоохранении позволяет существенно улучшить работу по профилактике офтальмологической заболеваемости и является одним из эффективных мероприятий диспансеризации студенческой молодежи, так как дает возможность выделить из числа обучающихся группу с высоким риском неблагоприятного исхода заболевания This article provides an overview of the main solutions available today for the formation of both short-term and long-term projections of the incidence of eye diseases and its adnexa in the student environment. On the other hand, there are a number of problems associated with a variety of factors affecting the incidence, statistical unreasonability and inconsistency of the available data analysis results. The results of mathematical modeling of the dependence of the incidence rate on the most influential factors of the educational and social environment are presented. The most important areas of developing mathematical models for the spread of morbidity are listed. With the help of the developed software package, a series of computational experiments was carried out to assess and predict the incidence of students in universities of various profiles. The effectiveness of the application of multivariate modeling and forecasting methods is shown, their limitations and practical application possibilities are indicated. By the location of the generalized region of favorable prognosis in the factor space, it is possible to determine the exposure time of factors unfavorable for vision, which should be no more than 10 ... 11 hours a day, the number of preventive measures should be at least 3 ... 4. At the same time, the risk of development myopia will be no more than 0.4, the probability of eye fatigue at the computer will be no more than 0.4, the likelihood of eye discomfort in the classroom will be no more than 0.15. Based on the nature of the forecast, the duration of the follow-up observation is determined, as well as the need for preventive measures to eliminate or weaken the action of adverse social, hygienic and biomedical factors of a particular patient. The use of the prognostic matrix in practical health care can significantly improve the work on the prevention of ophthalmic morbidity and is one of the effective medical examinations for students, since it makes it possible to distinguish among the students a group with a high risk of an unfavorable outcome of the disease

scholarly journals Public reporting of performance measures in long-term care in Canada: does it make a difference?

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
M Poldrugovac ◽  
J E Amuah ◽  
H Wei-Randall ◽  
P Sidhom ◽  
K Morris ◽  
...  
Keyword(s):  
Performance Measures ◽  
Performance Indicators ◽  
Long Term Care ◽  
Public Reporting ◽  
Policy Implications ◽  
Term Care ◽  
Factors Affecting ◽  
Care Facilities ◽  
The Impact

Abstract Background Evidence of the impact of public reporting of healthcare performance on quality improvement is not yet sufficient to draw conclusions with certainty, despite the important policy implications. This study explored the impact of implementing public reporting of performance indicators of long-term care facilities in Canada. The objective was to analyse whether improvements can be observed in performance measures after publication. Methods We considered 16 performance indicators in long-term care in Canada, 8 of which are publicly reported at a facility level, while the other 8 are privately reported. We analysed data from the Continuing Care Reporting System managed by the Canadian Institute for Health Information and based on information collection with RAI-MDS 2.0 © between the fiscal years 2011 and 2018. A multilevel model was developed to analyse time trends, before and after publication, which started in 2015. The analysis was also stratified by key sample characteristics, such as the facilities' jurisdiction, size, urban or rural location and performance prior to publication. Results Data from 1087 long-term care facilities were included. Among the 8 publicly reported indicators, the trend in the period after publication did not change significantly in 5 cases, improved in 2 cases and worsened in 1 case. Among the 8 privately reported indicators, no change was observed in 7, and worsening in 1 indicator. The stratification of the data suggests that for those indicators that were already improving prior to public reporting, there was either no change in trend or there was a decrease in the rate of improvement after publication. For those indicators that showed a worsening trend prior to public reporting, the contrary was observed. Conclusions Our findings suggest public reporting of performance data can support change. The trends of performance indicators prior to publication appear to have an impact on whether further change will occur after publication. Key messages Public reporting is likely one of the factors affecting change in performance in long-term care facilities. Public reporting of performance measures in long-term care facilities may support improvements in particular in cases where improvement was not observed before publication.

scholarly journals Impact of the COVID-19 pandemic: a perspective from industry

2020 ◽  
Vol 22 (Supplement_P) ◽  
pp. P56-P59
Author(s):  
Nick E J West ◽  
Wai-Fung Cheong ◽  
Els Boone ◽  
Neil E Moat
Keyword(s):  
Care Delivery ◽  
Models Of Care ◽  
Medium Term ◽  
Change Models ◽  
New Device ◽  
Supply Chain Logistics ◽  
Cardiovascular Patients ◽  
Medical Crisis ◽  
Device Industry

Abstract The global COVID-19 pandemic has led to unprecedented change throughout society.1 As the articles in this supplement outline, all segments of the broader cardiovascular community have been forced to adapt, to change models of care delivery, and to evolve and innovate in order to deliver optimal management for cardiovascular patients. The medtech/device industry has not been exempt from such change and has been forced to navigate direct and indirect COVID-associated disruption, with effects felt from supply chain logistics to the entire product lifecycle, from the running of clinical trials to new device approvals and managing training, proctoring and congresses in an increasingly-online world. This sea-change in circumstances itself has enforced the industry, in effect, to disrupt its own processes, models and activities. Whilst some of these changes may be temporary, many will endure for some time and some will doubtless become permanent; one thing is for sure: the healthcare ecosystem, including the medical device industry, will never look quite the same again. Although the pandemic has brought a short- to medium-term medical crisis to many countries, its role as a powerful disruptor cannot be underestimated, and may indeed prove to be a force for long-term good, given the accelerated innovation and rapid adaptation that it has cultivated.

scholarly journals Long-Term Data Traffic Forecasting for Network Dimensioning in LTE with Short Time Series

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1151
Author(s):  
Carolina Gijón ◽  
Matías Toril ◽  
Salvador Luna-Ramírez ◽  
María Luisa Marí-Altozano ◽  
José María Ruiz-Avilés
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Analysis ◽  
Data Traffic ◽  
Medium Term ◽  
Short Term ◽  
Traffic Forecasting ◽  
Series Analysis ◽  
Radio Planning

Network dimensioning is a critical task in current mobile networks, as any failure in this process leads to degraded user experience or unnecessary upgrades of network resources. For this purpose, radio planning tools often predict monthly busy-hour data traffic to detect capacity bottlenecks in advance. Supervised Learning (SL) arises as a promising solution to improve predictions obtained with legacy approaches. Previous works have shown that deep learning outperforms classical time series analysis when predicting data traffic in cellular networks in the short term (seconds/minutes) and medium term (hours/days) from long historical data series. However, long-term forecasting (several months horizon) performed in radio planning tools relies on short and noisy time series, thus requiring a separate analysis. In this work, we present the first study comparing SL and time series analysis approaches to predict monthly busy-hour data traffic on a cell basis in a live LTE network. To this end, an extensive dataset is collected, comprising data traffic per cell for a whole country during 30 months. The considered methods include Random Forest, different Neural Networks, Support Vector Regression, Seasonal Auto Regressive Integrated Moving Average and Additive Holt–Winters. Results show that SL models outperform time series approaches, while reducing data storage capacity requirements. More importantly, unlike in short-term and medium-term traffic forecasting, non-deep SL approaches are competitive with deep learning while being more computationally efficient.

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